Modified recommender system model for the utilized eLearning platform

Černá, Miloslava

doi:10.1007/s40692-019-00133-9

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…Cerna et al [13] designed an innovative system that aims to impose an order in managing electronic course content accompanying topics related to the geography, history, and culture of English-speaking countries. eir proposed approach is a blended learning model that uses LMS tools for communication, navigation, and evaluation of student presentations.…”

Section: Adaptive Learning and Recommended Systemsmentioning

confidence: 99%

“…In this context, many researchers have concentrated their efforts on developing new solutions to address these challenges. e first researchers' wave is oriented towards adaptive learning mode and recommended systems as solutions for these challenges, such as [7][8][9][10][11][12] and [13]. ey state that adaptive education systems can significantly support students' achievements via customized environments (i.e., teaching processes, materials, and assessments).…”

Section: Introductionmentioning

confidence: 99%

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Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Smirani

Yamani

Menzli

et al. 2022

Scientific Programming

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One of the challenges in e-learning is the customization of the learning environment to avoid learners’ failures. This paper proposes a Stacked Generalization for Failure Prediction (SGFP) model to improve students’ results. The SGFP model mixes three ensemble learning classifiers, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Random Forest (RF), using a Multilayer Perceptron (MLP). In fact, the model relies on high-quality training and testing datasets that are collected automatically from the analytic reports of the Blackboard Learning Management System (i.e., analytic for learn (A4L) and full grade center (FGC) modules. The SGFP algorithm was validated using heterogeneous data reflecting students’ interactivity degrees, educational performance, and skills. The main output of SGFP is a classification of students into three performance-based classes (class A: above average, class B: average, class C: below average). To avoid failures, the SGFP model uses the Blackboard Adaptive Release tool to design three learning paths where students have to follow automatically according to the class they belong to. The SGFP model was compared to base classifiers (LGBM, XGB, and RF). The results show that the mean and median accuracies of SGFP are higher. Moreover, it correctly identified students’ classifications with a sensitivity average of 97.3% and a precision average of 97.2%. Furthermore, SGFP had the highest F1-score of 97.1%. In addition, the used meta-classifier MLP has more accuracy than other Artificial Neural Network (ANN) algorithms, with an average of 97.3%. Once learned, tested, and validated, SGFP was applied to students before the end of the first semester of the 2020-2021 academic year at the College of Computer Sciences at Umm al-Qura University. The findings showed a significant increase in student success rates (98.86%). The drop rate declines from 12% to 1.14% for students in class C, for whom more customized assessment steps and materials are provided. SGFP outcomes may be beneficial for higher educational institutions within fully online or blended learning schemas to reduce the failure rate and improve the performance of program curriculum outcomes, especially in pandemic situations.

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Section: Adaptive Learning and Recommended Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Smirani

Yamani

Menzli

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

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“…M-learning systems were developed under the influence of studies in the area of E-learning, intelligent tutoring systems, adaptive learning, and computer-aided learning [24]. Most m-learning system architectures are an extension of E-learning architectures [25,26]. Context discovery, user profiles, user tracking, content management, and semantic indexing are key elements in developing adaptive m-learning systems.…”

Section: Related Studies (Learner Modeling Approaches)mentioning

confidence: 99%

Deep neural network based m-learning model for predicting mobile learners’ performance

Adnan¹,

Habib²,

Ashraf³

et al. 2020

Turk J Elec Eng & Comp Sci

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The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features on the dependent feature i.e. learners' performance. The m-learning model dynamically and intuitively explores the weights of optimum learning features on learning performance for different learners in their learning environment. Then it split learners into different groups based on features differences, weights, and interrelationships. Because of the high accuracy of the DL technique, it was used to classify learners into five different groups whereas random forest (RF) ensemble method was used in determining each feature importance in making adaptive m-learning model. Our experimental study also revealed that the m-learning model was successful in helping m-learners in increasing their performance and taking the right decision during the learning flow.

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“…Multiple Recommender System approaches are employed to cater to different kinds of needs in different websites. Over the years, there has been a drastic growth in the methods used to improve recommendation results for different purposes [2]- [8]. Recommendation systems can be broadly classified into two types, content-based [9]- [14] and collaborative filteringbased [15]- [20].…”

Section: Introductionmentioning

confidence: 99%

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

et al. 2020

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The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods.

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Modified recommender system model for the utilized eLearning platform

Cited by 14 publications

References 20 publications

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Deep neural network based m-learning model for predicting mobile learners’ performance

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

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